The purpose of this course is to get students up-to-speed regarding the practice of modern Bayesian econometrics. The course is divided into two modules, with the first module serving as an introduction to basic Bayesian principles and posterior computation. The second module illustrates the use of MCMC methods in an array of models. Throughout the courses students will be responsible for programming posterior simulators using real and artificial data.

The images on this page were created by (and then stolen from) one of my Bayesian heroes, Dale Poirier